3D Model Correction via Medial Axis Metrology

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Solution Overview

Problem

Current 3D printing technologies lack effective tools for predicting and correcting defects in 3D models before printing, leading to inefficiencies and material waste due to poor understanding of printing parameters and limitations, and existing software often fails to simulate the actual printed output accurately.

Innovation Solution

A system for automated metrology, measurement, and model correction that uses processors to calculate medial axis transforms, determine local feature sizes, and simulate printing to identify regions requiring material adjustments, thereby ensuring manufacturability and providing interactive visual feedback on potential deviations from the design intent.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If manual prediction and correction of defects is performed, then time and material waste are reduced, but the ability to predict and correct defects becomes increasingly difficult as the number of printers, materials, and manufacturing services grows

Engineering Contradiction:
Improvetime for prediction and correctionVSAvoidcomplexity of predicting and correcting defects
Core Design Contradiction:
Loss of timeVSDevice complexity

Solution Approach 1:

The system performs automated defect prediction and correction without requiring manual expert intervention. The software autonomously analyzes the 3D model, simulates the printing process, identifies potential defects, and generates corrected models, allowing the system to serve itself rather than relying on human operators to manually predict and correct defects

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The system changes the approach from manual parameter adjustment to automated parameter optimization by using algorithms to analyze printing parameters, material properties, and printer characteristics, then automatically adjusting model parameters to prevent defects based on simulated printing outcomes

Inventive Principle:
Principle #35Parameter changes

2Loss of information

If existing software solutions are used to preview 3D models, then model preparation is enabled, but no geometric differences or realistic rendering of the 3D printed object are shown

Engineering Contradiction:
Improveinformation about geometric differencesVSAvoidcomplexity of simulation software
Core Design Contradiction:
Loss of informationVSDevice complexity

Solution Approach 1:

The system creates a virtual copy of the printing process by simulating how the 3D model will be printed layer by layer, generating a digital representation of the expected printed output that shows geometric differences between the original model and the printed object, allowing users to preview defects before actual printing

Inventive Principle:
Principle #26Copying

Solution Approach 2:

The system introduces a simulation engine as an intermediary between the 3D model and the final printed object, which acts as a virtual mediator that translates the digital model into a predicted printed representation, showing how the model will actually print including defects and geometric deviations

Inventive Principle:
Principle #24Intermediary (Mediator)

3Adaptability or versatility

If 3D printing is used to produce complex geometries, then manufacturing flexibility increases, but print failures occur due to poor understanding of printing parameters

Engineering Contradiction:
Improveability to produce complex geometriesVSAvoidprint success rate
Core Design Contradiction:
Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary defect prediction and model correction before the actual printing process by simulating the printing operation in advance, identifying potential failures related to complex geometries, and automatically generating corrected models to ensure print success before material is committed

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system provides feedback about potential print failures and geometric issues by comparing the original 3D model with the simulated printed output, showing users where defects will occur due to complex geometries and printing parameters, and offering automated corrections to improve print reliability

Inventive Principle:
Principle #23Feedback

4Manufacturing precision

If multiple print attempts are made to achieve desired output, then print quality improves, but material waste and time consumption increase

Engineering Contradiction:
Improveprint qualityVSAvoidmaterial waste from failed prints
Core Design Contradiction:
Manufacturing precisionVSLoss of substance

Solution Approach 1:

The system performs preliminary defect prediction and model correction before printing by simulating the printing process and identifying potential quality issues in advance, allowing users to correct model defects digitally before committing material to failed print attempts

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system creates a virtual preview of the printed object that shows expected quality and defects, allowing users to evaluate print quality expectations before actual printing and make necessary corrections to the digital model, reducing the need for multiple physical print attempts

Inventive Principle:
Principle #26Copying

Data Source

PatentEP2930694B1Automated metrology and model correction for three dimensional (3D) printability
Publication Date: 2019.12.04 PALO ALTO RESEARCH CENTER INC
  • EP2930694B1 patent drawingFigure 1
  • EP2930694B1 patent drawingFigure 2
  • EP2930694B1 patent drawingFigure 3~4B

AI summary

A system and a method automate metrology, measurement, and model correction of a three dimensional (3D) model for 3D printability. Slices of the 3D model are received or generated. The slices represent 2D solids of the 3D model to be printed in corresponding print layers. Medial axis transforms of the slices are calculated. The medial axis transforms represent the slices in terms of corresponding medial axes. A local feature size at any point along a boundary of the slices is determined as the shortest distance from the point to a corresponding medial axis.